DeepSeek might not be such good news for energy after all
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The prompt asking whether it’s okay to lie generated a 1,000-word response from the DeepSeek model, which took 17,800 joules to generate—about what it takes to stream a 10-minute YouTube video. This was about 41% more energy than Meta’s model used to answer the prompt. Overall, when tested on 40 prompts, DeepSeek was found to have a similar energy efficiency to the Meta model, but DeepSeek tended to generate much longer responses and therefore was found to use 87% more energy.
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T [email protected] shared this topic
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The original claims of energy efficiency came from mixing up the energy usage of their much smaller model with their big model I think.
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So the answer, as always, is ban useless, power-sucking, unreliable, copyright-infringing AI.
That's naive. It's way too late for any of that. If some country decided to ban AI, all the engineers will just move somewhere else.
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And here I thought that the energy consumption was in the training.
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The FUD is hilarious. Even an llm would tell you the article compares apples and oranges... FFS.
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Everyone is making way too much money off of this for a blanket ban to ever happen.
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It's more like comparing them while they use the same fuel (as the article directly compares them in joules): Let's say the train also uses gasoline. The car is a far more "independent", controllable, and "doesn't waste fuel driving to places you don't want to go" and thus seen as "better" and more appealing, but that wide appeal and thus wide usage creates far more demand for gasoline, dries up the planet, and clogs up the streets, wasting fuel idling at traffic stops.
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Longer!=Detailed
Generally what they're calling out is that DeepSeek currently rambles more. With LLMs the challenge is how to get the right answer most sussinctly because each extra word is a lot of time/money.
That being said, I suspect that really it's all roughly the same. We've been seeing this back and forth with LLMs for a while and DeepSeek, while using a different approach, doesn't really break the mold.
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Yeah, I was thinking diesel powered trains
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The AI models use the same fuel for energy.
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Yes, sorry, where I live it's pretty normal for cars to be diesel powered. I agree with you!
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This is more about the "reasoning" aspect of the model where it outputs a bunch of "thinking" before the actual result. In a lot of cases it easily adds 2-3x onto the number of tokens needed to be generated. This isn't really useful output. It the model getting into a state where it can better respond.
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A bit flawed. What if the same prompts are used but both models are required to keep their responses equally brief?
